Flood forecasting with machine Learning, data Assimilation and Semi-pHysical modeling

ANR FLASH project (2009-2013) intends to capitalize on the advantages of machine learning methods in order to provide tools for real-time flash floods forecasting. In a first step, water level forecasts were provided based on rain estimation of rainfalls, leading to the design of a demonstrating software. In a second step, weather RADAR measurements will be taken in advantage, as for rainfall estimation than for directly inputs reflectivity to the model. Comparison between the 3-type of inputs (rain gauge rainfall, RADAR rainfall, COMEPHORE reanalysis) will be assessed.

Domaines

Sciences de l'ingénieur [physics]

Fichier principal

POSTER_FLASH-ERAD_2012_15-06-2012.pdf (5.98 Mo)

Origine : Fichiers produits par l'(les) auteur(s)

Administrateur IMT - Mines Alès : Connectez-vous pour contacter le contributeur

https://hal.mines-ales.fr/hal-03341863

Soumis le : lundi 13 septembre 2021-08:39:08

Dernière modification le : mardi 28 février 2023-15:36:21

Dates et versions

hal-03341863 , version 1 (13-09-2021)

Identifiants

HAL Id : hal-03341863 , version 1

Citer

Yann Visserot, Guillaume Artigue, Pierre-Alain Ayral, Audrey Bornancin-Plantier, Anne Johannet, et al.. Flood forecasting with machine Learning, data Assimilation and Semi-pHysical modeling. ERAD 2012 - The 7th European Conference on radar in Meteorology and Hydrology, Jun 2012, Toulouse, France. 35, pp.178 - 189, 2012. ⟨hal-03341863⟩

Exporter

BibTeX TEI Dublin Core DC Terms EndNote Datacite

Collections

INSTITUT-TELECOM EM-ALES AGROPOLIS

43 Consultations

44 Téléchargements